@CONFERENCE{lee.16,
  author = {Lee, J. and Rozell, C.J.},
  title = {Precision Cell Boundary Tracking on {DIC} Microscopy Video for Patch Clamping},
  booktitle = {{Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}},
  year = {2017},
  address = {New Orleans, LA},
  month = mar,
  abstract = {One method of patch clamping on brain tissue slices \emph{in vitro} requires a human operator to visually track a cell's boundary and delicately make contact on a cell's membrane using a micropipette's tip. This type of patch clamping may be automated with computer vision methods; yet this is challenging since it requires precision cell-boundary tracking in the presence of heavy noise and interference. In this work, we present a cell-boundary tracking computer vision system which employs a novel deconvolution algorithm specifically created for this application. The deconvolution algorithm was designed to exploit static and dynamic structure in the cell's edges using a reweighted edge-sparsity prior. Quantitative results on simulated data demonstrate the superiority of the proposed algorithm against previous state-of-the-art algorithms. Lastly, the algorithm is applied on real patch clamping video data and qualitative results are discussed.}
  }